Applied Psychological Measurement

 

Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Register here to gain access to SAGE's 500+ Journals Online

Click here for more information on The Virtual Advisor

Sign In to gain access to subscriptions and/or personal tools.
This Article
Right arrow Free Full Text (Free PDF) Free
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Budescu, D. V.
Right arrow Articles by Ben-Simon, A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?
Applied Psychological Measurement, Vol. 21, No. 3, 233-252 (1997)
DOI: 10.1177/01466216970213004

A Revised Modified Parallel Analysis for the Construction of Unidimensional Item Pools

David V. Budescu

University of Illinois

Yoav Cohen

Anat Ben-Simon

National Institute for Testing and Evaluation, Israel

Modified parallel analysis (MPA) is a heuristic method for assessing "approximate unidimensionality" of item pools. It compares the second eigenvalue of the observed correlation matrix with the corresponding eigenvalue extracted from a "parallel" matrix generated by a unidimensional and locally independent model. Revised MPA (RMPA) generalizes MPA and alleviates some of its technical limitations. RMPA includes an important and useful feature for eliminating items that violate the test's unidimensionality. This is achieved by eliminating items, one at a time, to determine their contribution to the matrices' eigenvalues. A test for detecting items with large impact in the observed dataset and then eliminating them is proposed. The new method was tested in several simulations in which unidimensional item pools were "contaminated" by various proportions of items from a secondary pool. The results indicate that RMPA does an excellent job of detecting low (10%) and moderate (25%) levels of contamination, but fails in cases of maximal (50%) contamination.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 JSLHRHome page
J. B. Tomblin and X. Zhang
The Dimensionality of Language Ability in School-Age Children
J Speech Lang Hear Res, December 1, 2006; 49(6): 1193 - 1208.
[Abstract] [Full Text] [PDF]

Home page
 Educational and Psychological MeasurementHome page
L.-J. Weng and C.-P. Cheng
Parallel Analysis with Unidimensional Binary Data
Educational and Psychological Measurement, October 1, 2005; 65(5): 697 - 716.
[Abstract] [PDF]